11 papers
Language Orthogonalization of Self-Supervised Speech Representations for Cross-lingual Parkinson's Detection
Minu Kim, Eunjung Yeo, Kwanghee Choi +1
Self-supervised speech models (S3Ms) provide powerful representations for Parkinson's disease (PD) detection, making cross-lingual transfer attractive for languages lacking labeled…
Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification
Hemansh Shridhar, Miika Toikkanen, June-Woo Kim
Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST). While effective…
Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
Yoon Tae Kim, Heejoon Koo, Miika Toikkanen +1
We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framewor…
A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction
June-Woo Kim, Kangwook Jang, Minu Kim +1
Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the Neck…
Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
Heejoon Koo, Yoon Tae Kim, Miika Toikkanen +1
AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inter-stethoscope variability. We…
Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
June-Woo Kim, Miika Toikkanen, Heejoon Koo +3
Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble methods can improve robustness, bu…
Understanding Frechet Speech Distance for Synthetic Speech Quality Evaluation
June-Woo Kim, Dhruv Agarwal, Federica Cerina
Objective evaluation of synthetic speech quality remains a critical challenge. Human listening tests are the gold standard, but costly and impractical at scale. Fréchet Distance ha…
Empowering Multimodal Respiratory Sound Classification with Counterfactual Adversarial Debiasing for Out-of-Distribution Robustness
Heejoon Koo, Miika Toikkanen, Yoon Tae Kim +2
Multimodal respiratory sound classification offers promise for early pulmonary disease detection by integrating bioacoustic signals with patient metadata. Nevertheless, current app…
Improving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles
Miika Toikkanen, June-Woo Kim
Respiratory sound datasets are limited in size and quality, making high performance difficult to achieve. Ensemble models help but inevitably increase compute cost at inference tim…
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
June-Woo Kim, Sanghoon Lee, Miika Toikkanen +2
Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis…
Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection
Myeonghoon Ryu, June-Woo Kim, Minseok Oh +2
Keyword spotting is often implemented by keyword classifier to the encoder in acoustic models, enabling the classification of predefined or open vocabulary keywords. Although keywo…